{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Neural Audio Synthesis with NSynth\n",
    "\n",
    "## Parag Mital"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot to explore with NSynth.  This notebook explores just a taste of what's possible including how to encode and decode, timestretch, and interpolate sounds.  Also check out the [blog post](https://magenta.tensorflow.org/nsynth-fastgen) for more examples including two compositions created with Ableton Live.  If you are interested in learning more, checkout my [online course on Kadenze](https://www.kadenze.com/programs/creative-applications-of-deep-learning-with-tensorflow) where we talk about Magenta and NSynth in more depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Encoding and Decoding\n",
    "\n",
    "We'll walkthrough using the source code to encode and decode some audio.  This is the most basic thing we can do with NSynth, and it will take at least about 6 minutes per 1 second of audio to perform on a GPU, though this will get faster!\n",
    "\n",
    "I'll first show you how to encode some audio.  This is basically saying, here is some audio, now put it into the trained model.  It's like the encoding of an MP3 file.  It takes some raw audio, and represents it using some really reduced down representation of the raw audio.  NSynth works similarly, but we can actually mess with the encoding to do some awesome stuff.  You can for instance, mix it with other encodings, or slow it down, or speed it up.  You can potentially even remove parts of it, mix many different encodings together, and hopefully just explore ideas yet to be thought of.  After you've created your encoding, you have to just generate, or decode it, just like what an audio player does to an MP3 file.\n",
    "\n",
    "First, to install Magenta, follow their setup guide here: https://github.com/tensorflow/magenta#installation - then import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from magenta.models.nsynth import utils\n",
    "from magenta.models.nsynth.wavenet import fastgen\n",
    "from IPython.display import Audio\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll load up a sound I downloaded from freesound.org.  The `utils.load_audio` method will resample this to the required sample rate of 16000.  I'll load in 40000 samples of this beat which should end up being a pretty good loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40000 samples, 2.5 seconds\n"
     ]
    }
   ],
   "source": [
    "# from https://www.freesound.org/people/MustardPlug/sounds/395058/\n",
    "fname = '395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav'\n",
    "sr = 16000\n",
    "audio = utils.load_audio(fname, sample_length=40000, sr=sr)\n",
    "sample_length = audio.shape[0]\n",
    "print('{} samples, {} seconds'.format(sample_length, sample_length / float(sr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding\n",
    "\n",
    "We'll now encode some audio using the pre-trained NSynth model (download from: http://download.magenta.tensorflow.org/models/nsynth/wavenet-ckpt.tar).  This is pretty fast, and takes about 3 seconds per 1 second of audio on my NVidia 1080 GPU.  This will give us a 125 x 16 dimension encoding for every 4 seconds of audio which we can then decode, or resynthesize.  We'll try a few things, including just leaving it alone and reconstructing it as is.  But then we'll also try some fun transformations of the encoding and see what's possible from there.\n",
    "\n",
    "```help(fastgen.encode)\n",
    "Help on function encode in module magenta.models.nsynth.wavenet.fastgen:\n",
    "\n",
    "encode(wav_data, checkpoint_path, sample_length=64000)\n",
    "    Generate an array of embeddings from an array of audio.\n",
    "      Args:\n",
    "        wav_data: Numpy array [batch_size, sample_length]\n",
    "        checkpoint_path: Location of the pretrained model.\n",
    "        sample_length: The total length of the final wave file, padded with 0s.\n",
    "      Returns:\n",
    "        encoding: a [mb, 125, 16] encoding (for 64000 sample audio file).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model.ckpt-200000\n",
      "CPU times: user 1min 4s, sys: 3.23 s, total: 1min 7s\n",
      "Wall time: 26.1 s\n"
     ]
    }
   ],
   "source": [
    "%time encoding = fastgen.encode(audio, 'model.ckpt-200000', sample_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a 3-dimensional tensor representing the encoding of the audio.  The first dimension of the encoding represents the batch dimension.  We could have passed in many audio files at once and the process would be much faster.  For now we've just passed in one audio file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 78, 16)\n"
     ]
    }
   ],
   "source": [
    "print(encoding.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also save the encoding so that we can use it again later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save(fname + '.npy', encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the encoding of this audio file.  Think of these as 16 channels of sounds all mixed together (though with a lot of caveats):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x125966310>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYF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\n7X+r3821Wnm+zQwwLiOOGJUiijRI4Y1CjbXcUUUAFFFFAH//2Q==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba6bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(20, 7))\n",
    "axs[0].plot(audio);\n",
    "axs[0].set_title('Audio Signal')\n",
    "axs[1].plot(encoding[0]);\n",
    "axs[1].set_title('NSynth Encoding')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should be able to pretty clearly see a sort of beat like pattern in both the signal and the encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a9fad50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axs[1].plot(encoding[0]);\n",
    "axs[1].set_title('NSynth Encoding')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoding\n",
    "\n",
    "Now we can decode the encodings as is.  This is the process that takes awhile, though it used to be so long that you wouldn't even dare trying it.  There is still plenty of room for improvement and I'm sure it will get faster very soon.\n",
    "\n",
    "```\n",
    "help(fastgen.synthesize)\n",
    "Help on function synthesize in module magenta.models.nsynth.wavenet.fastgen:\n",
    "\n",
    "synthesize(encodings, save_paths, checkpoint_path='model.ckpt-200000', samples_per_save=1000)\n",
    "    Synthesize audio from an array of embeddings.\n",
    "      Args:\n",
    "        encodings: Numpy array with shape [batch_size, time, dim].\n",
    "        save_paths: Iterable of output file names.\n",
    "        checkpoint_path: Location of the pretrained model. [model.ckpt-200000]\n",
    "        samples_per_save: Save files after every amount of generated samples.\n",
    "```        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model.ckpt-200000\n",
      "INFO:tensorflow:Sample: 0\n",
      "INFO:tensorflow:Saving: gen_395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav\n",
      "INFO:tensorflow:Sample: 100\n",
      "INFO:tensorflow:Sample: 200\n",
      "INFO:tensorflow:Sample: 300\n",
      "INFO:tensorflow:Sample: 400\n",
      "INFO:tensorflow:Sample: 500\n",
      "INFO:tensorflow:Sample: 600\n",
      "INFO:tensorflow:Sample: 700\n",
      "INFO:tensorflow:Sample: 800\n",
      "INFO:tensorflow:Sample: 900\n",
      "INFO:tensorflow:Sample: 1000\n",
      "INFO:tensorflow:Sample: 1100\n",
      "INFO:tensorflow:Sample: 1200\n",
      "INFO:tensorflow:Sample: 1300\n",
      "INFO:tensorflow:Sample: 1400\n",
      "INFO:tensorflow:Sample: 1500\n",
      "INFO:tensorflow:Sample: 1600\n",
      "INFO:tensorflow:Sample: 1700\n",
      "INFO:tensorflow:Sample: 1800\n",
      "INFO:tensorflow:Sample: 1900\n",
      "INFO:tensorflow:Sample: 2000\n",
      "INFO:tensorflow:Sample: 2100\n",
      "INFO:tensorflow:Sample: 2200\n",
      "INFO:tensorflow:Sample: 2300\n",
      "INFO:tensorflow:Sample: 2400\n",
      "INFO:tensorflow:Sample: 2500\n",
      "INFO:tensorflow:Sample: 2600\n",
      "INFO:tensorflow:Sample: 2700\n",
      "INFO:tensorflow:Sample: 2800\n",
      "INFO:tensorflow:Sample: 2900\n",
      "INFO:tensorflow:Sample: 3000\n",
      "INFO:tensorflow:Sample: 3100\n",
      "INFO:tensorflow:Sample: 3200\n",
      "INFO:tensorflow:Sample: 3300\n",
      "INFO:tensorflow:Sample: 3400\n",
      "INFO:tensorflow:Sample: 3500\n",
      "INFO:tensorflow:Sample: 3600\n",
      "INFO:tensorflow:Sample: 3700\n",
      "INFO:tensorflow:Sample: 3800\n",
      "INFO:tensorflow:Sample: 3900\n",
      "INFO:tensorflow:Sample: 4000\n",
      "INFO:tensorflow:Sample: 4100\n",
      "INFO:tensorflow:Sample: 4200\n",
      "INFO:tensorflow:Sample: 4300\n",
      "INFO:tensorflow:Sample: 4400\n",
      "INFO:tensorflow:Sample: 4500\n",
      "INFO:tensorflow:Sample: 4600\n",
      "INFO:tensorflow:Sample: 4700\n",
      "INFO:tensorflow:Sample: 4800\n",
      "INFO:tensorflow:Sample: 4900\n",
      "INFO:tensorflow:Sample: 5000\n",
      "INFO:tensorflow:Sample: 5100\n",
      "INFO:tensorflow:Sample: 5200\n",
      "INFO:tensorflow:Sample: 5300\n",
      "INFO:tensorflow:Sample: 5400\n",
      "INFO:tensorflow:Sample: 5500\n",
      "INFO:tensorflow:Sample: 5600\n",
      "INFO:tensorflow:Sample: 5700\n",
      "INFO:tensorflow:Sample: 5800\n",
      "INFO:tensorflow:Sample: 5900\n",
      "INFO:tensorflow:Sample: 6000\n",
      "INFO:tensorflow:Sample: 6100\n",
      "INFO:tensorflow:Sample: 6200\n",
      "INFO:tensorflow:Sample: 6300\n",
      "INFO:tensorflow:Sample: 6400\n",
      "INFO:tensorflow:Sample: 6500\n",
      "INFO:tensorflow:Sample: 6600\n",
      "INFO:tensorflow:Sample: 6700\n",
      "INFO:tensorflow:Sample: 6800\n",
      "INFO:tensorflow:Sample: 6900\n",
      "INFO:tensorflow:Sample: 7000\n",
      "INFO:tensorflow:Sample: 7100\n",
      "INFO:tensorflow:Sample: 7200\n",
      "INFO:tensorflow:Sample: 7300\n",
      "INFO:tensorflow:Sample: 7400\n",
      "INFO:tensorflow:Sample: 7500\n",
      "INFO:tensorflow:Sample: 7600\n",
      "INFO:tensorflow:Sample: 7700\n",
      "INFO:tensorflow:Sample: 7800\n",
      "INFO:tensorflow:Sample: 7900\n",
      "INFO:tensorflow:Sample: 8000\n",
      "INFO:tensorflow:Sample: 8100\n",
      "INFO:tensorflow:Sample: 8200\n",
      "INFO:tensorflow:Sample: 8300\n",
      "INFO:tensorflow:Sample: 8400\n",
      "INFO:tensorflow:Sample: 8500\n",
      "INFO:tensorflow:Sample: 8600\n",
      "INFO:tensorflow:Sample: 8700\n",
      "INFO:tensorflow:Sample: 8800\n",
      "INFO:tensorflow:Sample: 8900\n",
      "INFO:tensorflow:Sample: 9000\n",
      "INFO:tensorflow:Sample: 9100\n",
      "INFO:tensorflow:Sample: 9200\n",
      "INFO:tensorflow:Sample: 9300\n",
      "INFO:tensorflow:Sample: 9400\n",
      "INFO:tensorflow:Sample: 9500\n",
      "INFO:tensorflow:Sample: 9600\n",
      "INFO:tensorflow:Sample: 9700\n",
      "INFO:tensorflow:Sample: 9800\n",
      "INFO:tensorflow:Sample: 9900\n",
      "INFO:tensorflow:Sample: 10000\n",
      "INFO:tensorflow:Sample: 10100\n",
      "INFO:tensorflow:Sample: 10200\n",
      "INFO:tensorflow:Sample: 10300\n",
      "INFO:tensorflow:Sample: 10400\n",
      "INFO:tensorflow:Sample: 10500\n",
      "INFO:tensorflow:Sample: 10600\n",
      "INFO:tensorflow:Sample: 10700\n",
      "INFO:tensorflow:Sample: 10800\n",
      "INFO:tensorflow:Sample: 10900\n",
      "INFO:tensorflow:Sample: 11000\n",
      "INFO:tensorflow:Sample: 11100\n",
      "INFO:tensorflow:Sample: 11200\n",
      "INFO:tensorflow:Sample: 11300\n",
      "INFO:tensorflow:Sample: 11400\n",
      "INFO:tensorflow:Sample: 11500\n",
      "INFO:tensorflow:Sample: 11600\n",
      "INFO:tensorflow:Sample: 11700\n",
      "INFO:tensorflow:Sample: 11800\n",
      "INFO:tensorflow:Sample: 11900\n",
      "INFO:tensorflow:Sample: 12000\n",
      "INFO:tensorflow:Sample: 12100\n",
      "INFO:tensorflow:Sample: 12200\n",
      "INFO:tensorflow:Sample: 12300\n",
      "INFO:tensorflow:Sample: 12400\n",
      "INFO:tensorflow:Sample: 12500\n",
      "INFO:tensorflow:Sample: 12600\n",
      "INFO:tensorflow:Sample: 12700\n",
      "INFO:tensorflow:Sample: 12800\n",
      "INFO:tensorflow:Sample: 12900\n",
      "INFO:tensorflow:Sample: 13000\n",
      "INFO:tensorflow:Sample: 13100\n",
      "INFO:tensorflow:Sample: 13200\n",
      "INFO:tensorflow:Sample: 13300\n",
      "INFO:tensorflow:Sample: 13400\n",
      "INFO:tensorflow:Sample: 13500\n",
      "INFO:tensorflow:Sample: 13600\n",
      "INFO:tensorflow:Sample: 13700\n",
      "INFO:tensorflow:Sample: 13800\n",
      "INFO:tensorflow:Sample: 13900\n",
      "INFO:tensorflow:Sample: 14000\n",
      "INFO:tensorflow:Sample: 14100\n",
      "INFO:tensorflow:Sample: 14200\n",
      "INFO:tensorflow:Sample: 14300\n",
      "INFO:tensorflow:Sample: 14400\n",
      "INFO:tensorflow:Sample: 14500\n",
      "INFO:tensorflow:Sample: 14600\n",
      "INFO:tensorflow:Sample: 14700\n",
      "INFO:tensorflow:Sample: 14800\n",
      "INFO:tensorflow:Sample: 14900\n",
      "INFO:tensorflow:Sample: 15000\n",
      "INFO:tensorflow:Sample: 15100\n",
      "INFO:tensorflow:Sample: 15200\n",
      "INFO:tensorflow:Sample: 15300\n",
      "INFO:tensorflow:Sample: 15400\n",
      "INFO:tensorflow:Sample: 15500\n",
      "INFO:tensorflow:Sample: 15600\n",
      "INFO:tensorflow:Sample: 15700\n",
      "INFO:tensorflow:Sample: 15800\n",
      "INFO:tensorflow:Sample: 15900\n",
      "INFO:tensorflow:Sample: 16000\n",
      "INFO:tensorflow:Sample: 16100\n",
      "INFO:tensorflow:Sample: 16200\n",
      "INFO:tensorflow:Sample: 16300\n",
      "INFO:tensorflow:Sample: 16400\n",
      "INFO:tensorflow:Sample: 16500\n",
      "INFO:tensorflow:Sample: 16600\n",
      "INFO:tensorflow:Sample: 16700\n",
      "INFO:tensorflow:Sample: 16800\n",
      "INFO:tensorflow:Sample: 16900\n",
      "INFO:tensorflow:Sample: 17000\n",
      "INFO:tensorflow:Sample: 17100\n",
      "INFO:tensorflow:Sample: 17200\n",
      "INFO:tensorflow:Sample: 17300\n",
      "INFO:tensorflow:Sample: 17400\n",
      "INFO:tensorflow:Sample: 17500\n",
      "INFO:tensorflow:Sample: 17600\n",
      "INFO:tensorflow:Sample: 17700\n",
      "INFO:tensorflow:Sample: 17800\n",
      "INFO:tensorflow:Sample: 17900\n",
      "INFO:tensorflow:Sample: 18000\n",
      "INFO:tensorflow:Sample: 18100\n",
      "INFO:tensorflow:Sample: 18200\n",
      "INFO:tensorflow:Sample: 18300\n",
      "INFO:tensorflow:Sample: 18400\n",
      "INFO:tensorflow:Sample: 18500\n",
      "INFO:tensorflow:Sample: 18600\n",
      "INFO:tensorflow:Sample: 18700\n",
      "INFO:tensorflow:Sample: 18800\n",
      "INFO:tensorflow:Sample: 18900\n",
      "INFO:tensorflow:Sample: 19000\n",
      "INFO:tensorflow:Sample: 19100\n",
      "INFO:tensorflow:Sample: 19200\n",
      "INFO:tensorflow:Sample: 19300\n",
      "INFO:tensorflow:Sample: 19400\n",
      "INFO:tensorflow:Sample: 19500\n",
      "INFO:tensorflow:Sample: 19600\n",
      "INFO:tensorflow:Sample: 19700\n",
      "INFO:tensorflow:Sample: 19800\n",
      "INFO:tensorflow:Sample: 19900\n",
      "INFO:tensorflow:Sample: 20000\n",
      "INFO:tensorflow:Sample: 20100\n",
      "INFO:tensorflow:Sample: 20200\n",
      "INFO:tensorflow:Sample: 20300\n",
      "INFO:tensorflow:Sample: 20400\n",
      "INFO:tensorflow:Sample: 20500\n",
      "INFO:tensorflow:Sample: 20600\n",
      "INFO:tensorflow:Sample: 20700\n",
      "INFO:tensorflow:Sample: 20800\n",
      "INFO:tensorflow:Sample: 20900\n",
      "INFO:tensorflow:Sample: 21000\n",
      "INFO:tensorflow:Sample: 21100\n",
      "INFO:tensorflow:Sample: 21200\n",
      "INFO:tensorflow:Sample: 21300\n",
      "INFO:tensorflow:Sample: 21400\n",
      "INFO:tensorflow:Sample: 21500\n",
      "INFO:tensorflow:Sample: 21600\n",
      "INFO:tensorflow:Sample: 21700\n",
      "INFO:tensorflow:Sample: 21800\n",
      "INFO:tensorflow:Sample: 21900\n",
      "INFO:tensorflow:Sample: 22000\n",
      "INFO:tensorflow:Sample: 22100\n",
      "INFO:tensorflow:Sample: 22200\n",
      "INFO:tensorflow:Sample: 22300\n",
      "INFO:tensorflow:Sample: 22400\n",
      "INFO:tensorflow:Sample: 22500\n",
      "INFO:tensorflow:Sample: 22600\n",
      "INFO:tensorflow:Sample: 22700\n",
      "INFO:tensorflow:Sample: 22800\n",
      "INFO:tensorflow:Sample: 22900\n",
      "INFO:tensorflow:Sample: 23000\n",
      "INFO:tensorflow:Sample: 23100\n",
      "INFO:tensorflow:Sample: 23200\n",
      "INFO:tensorflow:Sample: 23300\n",
      "INFO:tensorflow:Sample: 23400\n",
      "INFO:tensorflow:Sample: 23500\n",
      "INFO:tensorflow:Sample: 23600\n",
      "INFO:tensorflow:Sample: 23700\n",
      "INFO:tensorflow:Sample: 23800\n",
      "INFO:tensorflow:Sample: 23900\n",
      "INFO:tensorflow:Sample: 24000\n",
      "INFO:tensorflow:Sample: 24100\n",
      "INFO:tensorflow:Sample: 24200\n",
      "INFO:tensorflow:Sample: 24300\n",
      "INFO:tensorflow:Sample: 24400\n",
      "INFO:tensorflow:Sample: 24500\n",
      "INFO:tensorflow:Sample: 24600\n",
      "INFO:tensorflow:Sample: 24700\n",
      "INFO:tensorflow:Sample: 24800\n",
      "INFO:tensorflow:Sample: 24900\n",
      "INFO:tensorflow:Sample: 25000\n",
      "INFO:tensorflow:Sample: 25100\n",
      "INFO:tensorflow:Sample: 25200\n",
      "INFO:tensorflow:Sample: 25300\n",
      "INFO:tensorflow:Sample: 25400\n",
      "INFO:tensorflow:Sample: 25500\n",
      "INFO:tensorflow:Sample: 25600\n",
      "INFO:tensorflow:Sample: 25700\n",
      "INFO:tensorflow:Sample: 25800\n",
      "INFO:tensorflow:Sample: 25900\n",
      "INFO:tensorflow:Sample: 26000\n",
      "INFO:tensorflow:Sample: 26100\n",
      "INFO:tensorflow:Sample: 26200\n",
      "INFO:tensorflow:Sample: 26300\n",
      "INFO:tensorflow:Sample: 26400\n",
      "INFO:tensorflow:Sample: 26500\n",
      "INFO:tensorflow:Sample: 26600\n",
      "INFO:tensorflow:Sample: 26700\n",
      "INFO:tensorflow:Sample: 26800\n",
      "INFO:tensorflow:Sample: 26900\n",
      "INFO:tensorflow:Sample: 27000\n",
      "INFO:tensorflow:Sample: 27100\n",
      "INFO:tensorflow:Sample: 27200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Sample: 27300\n",
      "INFO:tensorflow:Sample: 27400\n",
      "INFO:tensorflow:Sample: 27500\n",
      "INFO:tensorflow:Sample: 27600\n",
      "INFO:tensorflow:Sample: 27700\n",
      "INFO:tensorflow:Sample: 27800\n",
      "INFO:tensorflow:Sample: 27900\n",
      "INFO:tensorflow:Sample: 28000\n",
      "INFO:tensorflow:Sample: 28100\n",
      "INFO:tensorflow:Sample: 28200\n",
      "INFO:tensorflow:Sample: 28300\n",
      "INFO:tensorflow:Sample: 28400\n",
      "INFO:tensorflow:Sample: 28500\n",
      "INFO:tensorflow:Sample: 28600\n",
      "INFO:tensorflow:Sample: 28700\n",
      "INFO:tensorflow:Sample: 28800\n",
      "INFO:tensorflow:Sample: 28900\n",
      "INFO:tensorflow:Sample: 29000\n",
      "INFO:tensorflow:Sample: 29100\n",
      "INFO:tensorflow:Sample: 29200\n",
      "INFO:tensorflow:Sample: 29300\n",
      "INFO:tensorflow:Sample: 29400\n",
      "INFO:tensorflow:Sample: 29500\n",
      "INFO:tensorflow:Sample: 29600\n",
      "INFO:tensorflow:Sample: 29700\n",
      "INFO:tensorflow:Sample: 29800\n",
      "INFO:tensorflow:Sample: 29900\n",
      "INFO:tensorflow:Sample: 30000\n",
      "INFO:tensorflow:Sample: 30100\n",
      "INFO:tensorflow:Sample: 30200\n",
      "INFO:tensorflow:Sample: 30300\n",
      "INFO:tensorflow:Sample: 30400\n",
      "INFO:tensorflow:Sample: 30500\n",
      "INFO:tensorflow:Sample: 30600\n",
      "INFO:tensorflow:Sample: 30700\n",
      "INFO:tensorflow:Sample: 30800\n",
      "INFO:tensorflow:Sample: 30900\n",
      "INFO:tensorflow:Sample: 31000\n",
      "INFO:tensorflow:Sample: 31100\n",
      "INFO:tensorflow:Sample: 31200\n",
      "INFO:tensorflow:Sample: 31300\n",
      "INFO:tensorflow:Sample: 31400\n",
      "INFO:tensorflow:Sample: 31500\n",
      "INFO:tensorflow:Sample: 31600\n",
      "INFO:tensorflow:Sample: 31700\n",
      "INFO:tensorflow:Sample: 31800\n",
      "INFO:tensorflow:Sample: 31900\n",
      "INFO:tensorflow:Sample: 32000\n",
      "INFO:tensorflow:Sample: 32100\n",
      "INFO:tensorflow:Sample: 32200\n",
      "INFO:tensorflow:Sample: 32300\n",
      "INFO:tensorflow:Sample: 32400\n",
      "INFO:tensorflow:Sample: 32500\n",
      "INFO:tensorflow:Sample: 32600\n",
      "INFO:tensorflow:Sample: 32700\n",
      "INFO:tensorflow:Sample: 32800\n",
      "INFO:tensorflow:Sample: 32900\n",
      "INFO:tensorflow:Sample: 33000\n",
      "INFO:tensorflow:Sample: 33100\n",
      "INFO:tensorflow:Sample: 33200\n",
      "INFO:tensorflow:Sample: 33300\n",
      "INFO:tensorflow:Sample: 33400\n",
      "INFO:tensorflow:Sample: 33500\n",
      "INFO:tensorflow:Sample: 33600\n",
      "INFO:tensorflow:Sample: 33700\n",
      "INFO:tensorflow:Sample: 33800\n",
      "INFO:tensorflow:Sample: 33900\n",
      "INFO:tensorflow:Sample: 34000\n",
      "INFO:tensorflow:Sample: 34100\n",
      "INFO:tensorflow:Sample: 34200\n",
      "INFO:tensorflow:Sample: 34300\n",
      "INFO:tensorflow:Sample: 34400\n",
      "INFO:tensorflow:Sample: 34500\n",
      "INFO:tensorflow:Sample: 34600\n",
      "INFO:tensorflow:Sample: 34700\n",
      "INFO:tensorflow:Sample: 34800\n",
      "INFO:tensorflow:Sample: 34900\n",
      "INFO:tensorflow:Sample: 35000\n",
      "INFO:tensorflow:Sample: 35100\n",
      "INFO:tensorflow:Sample: 35200\n",
      "INFO:tensorflow:Sample: 35300\n",
      "INFO:tensorflow:Sample: 35400\n",
      "INFO:tensorflow:Sample: 35500\n",
      "INFO:tensorflow:Sample: 35600\n",
      "INFO:tensorflow:Sample: 35700\n",
      "INFO:tensorflow:Sample: 35800\n",
      "INFO:tensorflow:Sample: 35900\n",
      "INFO:tensorflow:Sample: 36000\n",
      "INFO:tensorflow:Sample: 36100\n",
      "INFO:tensorflow:Sample: 36200\n",
      "INFO:tensorflow:Sample: 36300\n",
      "INFO:tensorflow:Sample: 36400\n",
      "INFO:tensorflow:Sample: 36500\n",
      "INFO:tensorflow:Sample: 36600\n",
      "INFO:tensorflow:Sample: 36700\n",
      "INFO:tensorflow:Sample: 36800\n",
      "INFO:tensorflow:Sample: 36900\n",
      "INFO:tensorflow:Sample: 37000\n",
      "INFO:tensorflow:Sample: 37100\n",
      "INFO:tensorflow:Sample: 37200\n",
      "INFO:tensorflow:Sample: 37300\n",
      "INFO:tensorflow:Sample: 37400\n",
      "INFO:tensorflow:Sample: 37500\n",
      "INFO:tensorflow:Sample: 37600\n",
      "INFO:tensorflow:Sample: 37700\n",
      "INFO:tensorflow:Sample: 37800\n",
      "INFO:tensorflow:Sample: 37900\n",
      "INFO:tensorflow:Sample: 38000\n",
      "INFO:tensorflow:Sample: 38100\n",
      "INFO:tensorflow:Sample: 38200\n",
      "INFO:tensorflow:Sample: 38300\n",
      "INFO:tensorflow:Sample: 38400\n",
      "INFO:tensorflow:Sample: 38500\n",
      "INFO:tensorflow:Sample: 38600\n",
      "INFO:tensorflow:Sample: 38700\n",
      "INFO:tensorflow:Sample: 38800\n",
      "INFO:tensorflow:Sample: 38900\n",
      "INFO:tensorflow:Sample: 39000\n",
      "INFO:tensorflow:Sample: 39100\n",
      "INFO:tensorflow:Sample: 39200\n",
      "INFO:tensorflow:Sample: 39300\n",
      "INFO:tensorflow:Sample: 39400\n",
      "INFO:tensorflow:Sample: 39500\n",
      "INFO:tensorflow:Sample: 39600\n",
      "INFO:tensorflow:Sample: 39700\n",
      "INFO:tensorflow:Sample: 39800\n",
      "INFO:tensorflow:Sample: 39900\n",
      "INFO:tensorflow:Saving: gen_395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav\n",
      "CPU times: user 1h 15min 10s, sys: 19min 2s, total: 1h 34min 12s\n",
      "Wall time: 24min 8s\n"
     ]
    }
   ],
   "source": [
    "%time fastgen.synthesize(encoding, save_paths=['gen_' + fname], samples_per_save=sample_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After it's done synthesizing, we can see that takes about 6 minutes per 1 second of audio on a non-optimized version of Tensorflow for GPU on an NVidia 1080 GPU.  We can speed things up considerably if we want to do multiple encodings at a time.  We'll see that in just a moment.  Let's first listen to the synthesized audio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sr = 16000\n",
    "synthesis = utils.load_audio('gen_' + fname, sample_length=sample_length, sr=sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listening to the audio, the sounds are definitely different.  NSynth seems to apply a sort of gobbly low-pass  that also really doesn't know what to do with the high frequencies.  It is really quite hard to describe, but that is what is so interesting about it.  It has a recognizable, characteristic sound.\n",
    "\n",
    "Let's try another one.  I'll put the whole workflow for synthesis in two cells, and we can listen to another synthesis of a vocalist singing, \"Laaaa\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_encoding(fname, sample_length=None, sr=16000, ckpt='model.ckpt-200000'):\n",
    "    audio = utils.load_audio(fname, sample_length=sample_length, sr=sr)\n",
    "    encoding = fastgen.encode(audio, ckpt, sample_length)\n",
    "    return audio, encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model.ckpt-200000\n",
      "INFO:tensorflow:Restoring parameters from model.ckpt-200000\n",
      "INFO:tensorflow:Sample: 0\n",
      "INFO:tensorflow:Saving: gen_213259__maurolupo__girl-sings-laa.wav\n",
      "INFO:tensorflow:Sample: 100\n",
      "INFO:tensorflow:Sample: 200\n",
      "INFO:tensorflow:Sample: 300\n",
      "INFO:tensorflow:Sample: 400\n",
      "INFO:tensorflow:Sample: 500\n",
      "INFO:tensorflow:Sample: 600\n",
      "INFO:tensorflow:Sample: 700\n",
      "INFO:tensorflow:Sample: 800\n",
      "INFO:tensorflow:Sample: 900\n",
      "INFO:tensorflow:Sample: 1000\n",
      "INFO:tensorflow:Sample: 1100\n",
      "INFO:tensorflow:Sample: 1200\n",
      "INFO:tensorflow:Sample: 1300\n",
      "INFO:tensorflow:Sample: 1400\n",
      "INFO:tensorflow:Sample: 1500\n",
      "INFO:tensorflow:Sample: 1600\n",
      "INFO:tensorflow:Sample: 1700\n",
      "INFO:tensorflow:Sample: 1800\n",
      "INFO:tensorflow:Sample: 1900\n",
      "INFO:tensorflow:Sample: 2000\n",
      "INFO:tensorflow:Sample: 2100\n",
      "INFO:tensorflow:Sample: 2200\n",
      "INFO:tensorflow:Sample: 2300\n",
      "INFO:tensorflow:Sample: 2400\n",
      "INFO:tensorflow:Sample: 2500\n",
      "INFO:tensorflow:Sample: 2600\n",
      "INFO:tensorflow:Sample: 2700\n",
      "INFO:tensorflow:Sample: 2800\n",
      "INFO:tensorflow:Sample: 2900\n",
      "INFO:tensorflow:Sample: 3000\n",
      "INFO:tensorflow:Sample: 3100\n",
      "INFO:tensorflow:Sample: 3200\n",
      "INFO:tensorflow:Sample: 3300\n",
      "INFO:tensorflow:Sample: 3400\n",
      "INFO:tensorflow:Sample: 3500\n",
      "INFO:tensorflow:Sample: 3600\n",
      "INFO:tensorflow:Sample: 3700\n",
      "INFO:tensorflow:Sample: 3800\n",
      "INFO:tensorflow:Sample: 3900\n",
      "INFO:tensorflow:Sample: 4000\n",
      "INFO:tensorflow:Sample: 4100\n",
      "INFO:tensorflow:Sample: 4200\n",
      "INFO:tensorflow:Sample: 4300\n",
      "INFO:tensorflow:Sample: 4400\n",
      "INFO:tensorflow:Sample: 4500\n",
      "INFO:tensorflow:Sample: 4600\n",
      "INFO:tensorflow:Sample: 4700\n",
      "INFO:tensorflow:Sample: 4800\n",
      "INFO:tensorflow:Sample: 4900\n",
      "INFO:tensorflow:Sample: 5000\n",
      "INFO:tensorflow:Sample: 5100\n",
      "INFO:tensorflow:Sample: 5200\n",
      "INFO:tensorflow:Sample: 5300\n",
      "INFO:tensorflow:Sample: 5400\n",
      "INFO:tensorflow:Sample: 5500\n",
      "INFO:tensorflow:Sample: 5600\n",
      "INFO:tensorflow:Sample: 5700\n",
      "INFO:tensorflow:Sample: 5800\n",
      "INFO:tensorflow:Sample: 5900\n",
      "INFO:tensorflow:Sample: 6000\n",
      "INFO:tensorflow:Sample: 6100\n",
      "INFO:tensorflow:Sample: 6200\n",
      "INFO:tensorflow:Sample: 6300\n",
      "INFO:tensorflow:Sample: 6400\n",
      "INFO:tensorflow:Sample: 6500\n",
      "INFO:tensorflow:Sample: 6600\n",
      "INFO:tensorflow:Sample: 6700\n",
      "INFO:tensorflow:Sample: 6800\n",
      "INFO:tensorflow:Sample: 6900\n",
      "INFO:tensorflow:Sample: 7000\n",
      "INFO:tensorflow:Sample: 7100\n",
      "INFO:tensorflow:Sample: 7200\n",
      "INFO:tensorflow:Sample: 7300\n",
      "INFO:tensorflow:Sample: 7400\n",
      "INFO:tensorflow:Sample: 7500\n",
      "INFO:tensorflow:Sample: 7600\n",
      "INFO:tensorflow:Sample: 7700\n",
      "INFO:tensorflow:Sample: 7800\n",
      "INFO:tensorflow:Sample: 7900\n",
      "INFO:tensorflow:Sample: 8000\n",
      "INFO:tensorflow:Sample: 8100\n",
      "INFO:tensorflow:Sample: 8200\n",
      "INFO:tensorflow:Sample: 8300\n",
      "INFO:tensorflow:Sample: 8400\n",
      "INFO:tensorflow:Sample: 8500\n",
      "INFO:tensorflow:Sample: 8600\n",
      "INFO:tensorflow:Sample: 8700\n",
      "INFO:tensorflow:Sample: 8800\n",
      "INFO:tensorflow:Sample: 8900\n",
      "INFO:tensorflow:Sample: 9000\n",
      "INFO:tensorflow:Sample: 9100\n",
      "INFO:tensorflow:Sample: 9200\n",
      "INFO:tensorflow:Sample: 9300\n",
      "INFO:tensorflow:Sample: 9400\n",
      "INFO:tensorflow:Sample: 9500\n",
      "INFO:tensorflow:Sample: 9600\n",
      "INFO:tensorflow:Sample: 9700\n",
      "INFO:tensorflow:Sample: 9800\n",
      "INFO:tensorflow:Sample: 9900\n",
      "INFO:tensorflow:Sample: 10000\n",
      "INFO:tensorflow:Sample: 10100\n",
      "INFO:tensorflow:Sample: 10200\n",
      "INFO:tensorflow:Sample: 10300\n",
      "INFO:tensorflow:Sample: 10400\n",
      "INFO:tensorflow:Sample: 10500\n",
      "INFO:tensorflow:Sample: 10600\n",
      "INFO:tensorflow:Sample: 10700\n",
      "INFO:tensorflow:Sample: 10800\n",
      "INFO:tensorflow:Sample: 10900\n",
      "INFO:tensorflow:Sample: 11000\n",
      "INFO:tensorflow:Sample: 11100\n",
      "INFO:tensorflow:Sample: 11200\n",
      "INFO:tensorflow:Sample: 11300\n",
      "INFO:tensorflow:Sample: 11400\n",
      "INFO:tensorflow:Sample: 11500\n",
      "INFO:tensorflow:Sample: 11600\n",
      "INFO:tensorflow:Sample: 11700\n",
      "INFO:tensorflow:Sample: 11800\n",
      "INFO:tensorflow:Sample: 11900\n",
      "INFO:tensorflow:Sample: 12000\n",
      "INFO:tensorflow:Sample: 12100\n",
      "INFO:tensorflow:Sample: 12200\n",
      "INFO:tensorflow:Sample: 12300\n",
      "INFO:tensorflow:Sample: 12400\n",
      "INFO:tensorflow:Sample: 12500\n",
      "INFO:tensorflow:Sample: 12600\n",
      "INFO:tensorflow:Sample: 12700\n",
      "INFO:tensorflow:Sample: 12800\n",
      "INFO:tensorflow:Sample: 12900\n",
      "INFO:tensorflow:Sample: 13000\n",
      "INFO:tensorflow:Sample: 13100\n",
      "INFO:tensorflow:Sample: 13200\n",
      "INFO:tensorflow:Sample: 13300\n",
      "INFO:tensorflow:Sample: 13400\n",
      "INFO:tensorflow:Sample: 13500\n",
      "INFO:tensorflow:Sample: 13600\n",
      "INFO:tensorflow:Sample: 13700\n",
      "INFO:tensorflow:Sample: 13800\n",
      "INFO:tensorflow:Sample: 13900\n",
      "INFO:tensorflow:Sample: 14000\n",
      "INFO:tensorflow:Sample: 14100\n",
      "INFO:tensorflow:Sample: 14200\n",
      "INFO:tensorflow:Sample: 14300\n",
      "INFO:tensorflow:Sample: 14400\n",
      "INFO:tensorflow:Sample: 14500\n",
      "INFO:tensorflow:Sample: 14600\n",
      "INFO:tensorflow:Sample: 14700\n",
      "INFO:tensorflow:Sample: 14800\n",
      "INFO:tensorflow:Sample: 14900\n",
      "INFO:tensorflow:Sample: 15000\n",
      "INFO:tensorflow:Sample: 15100\n",
      "INFO:tensorflow:Sample: 15200\n",
      "INFO:tensorflow:Sample: 15300\n",
      "INFO:tensorflow:Sample: 15400\n",
      "INFO:tensorflow:Sample: 15500\n",
      "INFO:tensorflow:Sample: 15600\n",
      "INFO:tensorflow:Sample: 15700\n",
      "INFO:tensorflow:Sample: 15800\n",
      "INFO:tensorflow:Sample: 15900\n",
      "INFO:tensorflow:Sample: 16000\n",
      "INFO:tensorflow:Sample: 16100\n",
      "INFO:tensorflow:Sample: 16200\n",
      "INFO:tensorflow:Sample: 16300\n",
      "INFO:tensorflow:Sample: 16400\n",
      "INFO:tensorflow:Sample: 16500\n",
      "INFO:tensorflow:Sample: 16600\n",
      "INFO:tensorflow:Sample: 16700\n",
      "INFO:tensorflow:Sample: 16800\n",
      "INFO:tensorflow:Sample: 16900\n",
      "INFO:tensorflow:Sample: 17000\n",
      "INFO:tensorflow:Sample: 17100\n",
      "INFO:tensorflow:Sample: 17200\n",
      "INFO:tensorflow:Sample: 17300\n",
      "INFO:tensorflow:Sample: 17400\n",
      "INFO:tensorflow:Sample: 17500\n",
      "INFO:tensorflow:Sample: 17600\n",
      "INFO:tensorflow:Sample: 17700\n",
      "INFO:tensorflow:Sample: 17800\n",
      "INFO:tensorflow:Sample: 17900\n",
      "INFO:tensorflow:Sample: 18000\n",
      "INFO:tensorflow:Sample: 18100\n",
      "INFO:tensorflow:Sample: 18200\n",
      "INFO:tensorflow:Sample: 18300\n",
      "INFO:tensorflow:Sample: 18400\n",
      "INFO:tensorflow:Sample: 18500\n",
      "INFO:tensorflow:Sample: 18600\n",
      "INFO:tensorflow:Sample: 18700\n",
      "INFO:tensorflow:Sample: 18800\n",
      "INFO:tensorflow:Sample: 18900\n",
      "INFO:tensorflow:Sample: 19000\n",
      "INFO:tensorflow:Sample: 19100\n",
      "INFO:tensorflow:Sample: 19200\n",
      "INFO:tensorflow:Sample: 19300\n",
      "INFO:tensorflow:Sample: 19400\n",
      "INFO:tensorflow:Sample: 19500\n",
      "INFO:tensorflow:Sample: 19600\n",
      "INFO:tensorflow:Sample: 19700\n",
      "INFO:tensorflow:Sample: 19800\n",
      "INFO:tensorflow:Sample: 19900\n",
      "INFO:tensorflow:Sample: 20000\n",
      "INFO:tensorflow:Sample: 20100\n",
      "INFO:tensorflow:Sample: 20200\n",
      "INFO:tensorflow:Sample: 20300\n",
      "INFO:tensorflow:Sample: 20400\n",
      "INFO:tensorflow:Sample: 20500\n",
      "INFO:tensorflow:Sample: 20600\n",
      "INFO:tensorflow:Sample: 20700\n",
      "INFO:tensorflow:Sample: 20800\n",
      "INFO:tensorflow:Sample: 20900\n",
      "INFO:tensorflow:Sample: 21000\n",
      "INFO:tensorflow:Sample: 21100\n",
      "INFO:tensorflow:Sample: 21200\n",
      "INFO:tensorflow:Sample: 21300\n",
      "INFO:tensorflow:Sample: 21400\n",
      "INFO:tensorflow:Sample: 21500\n",
      "INFO:tensorflow:Sample: 21600\n",
      "INFO:tensorflow:Sample: 21700\n",
      "INFO:tensorflow:Sample: 21800\n",
      "INFO:tensorflow:Sample: 21900\n",
      "INFO:tensorflow:Sample: 22000\n",
      "INFO:tensorflow:Sample: 22100\n",
      "INFO:tensorflow:Sample: 22200\n",
      "INFO:tensorflow:Sample: 22300\n",
      "INFO:tensorflow:Sample: 22400\n",
      "INFO:tensorflow:Sample: 22500\n",
      "INFO:tensorflow:Sample: 22600\n",
      "INFO:tensorflow:Sample: 22700\n",
      "INFO:tensorflow:Sample: 22800\n",
      "INFO:tensorflow:Sample: 22900\n",
      "INFO:tensorflow:Sample: 23000\n",
      "INFO:tensorflow:Sample: 23100\n",
      "INFO:tensorflow:Sample: 23200\n",
      "INFO:tensorflow:Sample: 23300\n",
      "INFO:tensorflow:Sample: 23400\n",
      "INFO:tensorflow:Sample: 23500\n",
      "INFO:tensorflow:Sample: 23600\n",
      "INFO:tensorflow:Sample: 23700\n",
      "INFO:tensorflow:Sample: 23800\n",
      "INFO:tensorflow:Sample: 23900\n",
      "INFO:tensorflow:Sample: 24000\n",
      "INFO:tensorflow:Sample: 24100\n",
      "INFO:tensorflow:Sample: 24200\n",
      "INFO:tensorflow:Sample: 24300\n",
      "INFO:tensorflow:Sample: 24400\n",
      "INFO:tensorflow:Sample: 24500\n",
      "INFO:tensorflow:Sample: 24600\n",
      "INFO:tensorflow:Sample: 24700\n",
      "INFO:tensorflow:Sample: 24800\n",
      "INFO:tensorflow:Sample: 24900\n",
      "INFO:tensorflow:Sample: 25000\n",
      "INFO:tensorflow:Sample: 25100\n",
      "INFO:tensorflow:Sample: 25200\n",
      "INFO:tensorflow:Sample: 25300\n",
      "INFO:tensorflow:Sample: 25400\n",
      "INFO:tensorflow:Sample: 25500\n",
      "INFO:tensorflow:Sample: 25600\n",
      "INFO:tensorflow:Sample: 25700\n",
      "INFO:tensorflow:Sample: 25800\n",
      "INFO:tensorflow:Sample: 25900\n",
      "INFO:tensorflow:Sample: 26000\n",
      "INFO:tensorflow:Sample: 26100\n",
      "INFO:tensorflow:Sample: 26200\n",
      "INFO:tensorflow:Sample: 26300\n",
      "INFO:tensorflow:Sample: 26400\n",
      "INFO:tensorflow:Sample: 26500\n",
      "INFO:tensorflow:Sample: 26600\n",
      "INFO:tensorflow:Sample: 26700\n",
      "INFO:tensorflow:Sample: 26800\n",
      "INFO:tensorflow:Sample: 26900\n",
      "INFO:tensorflow:Sample: 27000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Sample: 27100\n",
      "INFO:tensorflow:Sample: 27200\n",
      "INFO:tensorflow:Sample: 27300\n",
      "INFO:tensorflow:Sample: 27400\n",
      "INFO:tensorflow:Sample: 27500\n",
      "INFO:tensorflow:Sample: 27600\n",
      "INFO:tensorflow:Sample: 27700\n",
      "INFO:tensorflow:Sample: 27800\n",
      "INFO:tensorflow:Sample: 27900\n",
      "INFO:tensorflow:Sample: 28000\n",
      "INFO:tensorflow:Sample: 28100\n",
      "INFO:tensorflow:Sample: 28200\n",
      "INFO:tensorflow:Sample: 28300\n",
      "INFO:tensorflow:Sample: 28400\n",
      "INFO:tensorflow:Sample: 28500\n",
      "INFO:tensorflow:Sample: 28600\n",
      "INFO:tensorflow:Sample: 28700\n",
      "INFO:tensorflow:Sample: 28800\n",
      "INFO:tensorflow:Sample: 28900\n",
      "INFO:tensorflow:Sample: 29000\n",
      "INFO:tensorflow:Sample: 29100\n",
      "INFO:tensorflow:Sample: 29200\n",
      "INFO:tensorflow:Sample: 29300\n",
      "INFO:tensorflow:Sample: 29400\n",
      "INFO:tensorflow:Sample: 29500\n",
      "INFO:tensorflow:Sample: 29600\n",
      "INFO:tensorflow:Sample: 29700\n",
      "INFO:tensorflow:Sample: 29800\n",
      "INFO:tensorflow:Sample: 29900\n",
      "INFO:tensorflow:Sample: 30000\n",
      "INFO:tensorflow:Sample: 30100\n",
      "INFO:tensorflow:Sample: 30200\n",
      "INFO:tensorflow:Sample: 30300\n",
      "INFO:tensorflow:Sample: 30400\n",
      "INFO:tensorflow:Sample: 30500\n",
      "INFO:tensorflow:Sample: 30600\n",
      "INFO:tensorflow:Sample: 30700\n",
      "INFO:tensorflow:Sample: 30800\n",
      "INFO:tensorflow:Sample: 30900\n",
      "INFO:tensorflow:Sample: 31000\n",
      "INFO:tensorflow:Sample: 31100\n",
      "INFO:tensorflow:Sample: 31200\n",
      "INFO:tensorflow:Sample: 31300\n",
      "INFO:tensorflow:Sample: 31400\n",
      "INFO:tensorflow:Sample: 31500\n",
      "INFO:tensorflow:Sample: 31600\n",
      "INFO:tensorflow:Sample: 31700\n",
      "INFO:tensorflow:Saving: gen_213259__maurolupo__girl-sings-laa.wav\n"
     ]
    }
   ],
   "source": [
    "# from https://www.freesound.org/people/maurolupo/sounds/213259/\n",
    "fname = '213259__maurolupo__girl-sings-laa.wav'\n",
    "sample_length = 32000\n",
    "audio, encoding = load_encoding(fname, sample_length)\n",
    "fastgen.synthesize(\n",
    "    encoding,\n",
    "    save_paths=['gen_' + fname],\n",
    "    samples_per_save=sample_length)\n",
    "synthesis = utils.load_audio('gen_' + fname,\n",
    "                             sample_length=sample_length,\n",
    "                             sr=sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside from the quality of the reconstruction, what we're really after is what is possible with such a model.  Let's look at two examples now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Timestretching\n",
    "\n",
    "Let's try something more fun.  We'll stretch the encodings a bit and see what it sounds like.  If you were to try and stretch audio directly, you'd hear a pitch shift.  There are some other ways of stretching audio without shifting pitch, like granular synthesis.  But it turns out that NSynth can also timestretch.  Let's see how.  First we'll use image interpolation to help stretch the encodings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-image\n",
      "  Using cached scikit_image-0.13.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl\n",
      "Collecting networkx>=1.8 (from scikit-image)\n",
      "  Using cached networkx-1.11-py2.py3-none-any.whl\n",
      "Requirement already satisfied: scipy>=0.17.0 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from scikit-image)\n",
      "Collecting PyWavelets>=0.4.0 (from scikit-image)\n",
      "  Using cached PyWavelets-0.5.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl\n",
      "Requirement already satisfied: matplotlib>=1.3.1 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from scikit-image)\n",
      "Requirement already satisfied: six>=1.7.3 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from scikit-image)\n",
      "Requirement already satisfied: pillow>=2.1.0 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from scikit-image)\n",
      "Requirement already satisfied: decorator>=3.4.0 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from networkx>=1.8->scikit-image)\n",
      "Requirement already satisfied: numpy>=1.8.2 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from scipy>=0.17.0->scikit-image)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: pytz in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: python-dateutil in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: functools32 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: subprocess32 in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
      "Requirement already satisfied: olefile in /Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages (from pillow>=2.1.0->scikit-image)\n",
      "Installing collected packages: networkx, PyWavelets, scikit-image\n",
      "Successfully installed PyWavelets-0.5.2 networkx-1.11 scikit-image-0.13.0\n"
     ]
    }
   ],
   "source": [
    "# use image interpolation to stretch the encoding: (pip install scikit-image)\n",
    "try:\n",
    "    from skimage.transform import resize\n",
    "except ImportError:\n",
    "    !pip install scikit-image\n",
    "    from skimage.transform import resize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a utility function to help you stretch your own encoding.  It uses skimage.transform and will retain the range of values.  Images typically only have a range of 0-1, but the encodings aren't actually images so we'll keep track of their min/max in order to stretch them like images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def timestretch(encodings, factor):\n",
    "    min_encoding, max_encoding = encoding.min(), encoding.max()\n",
    "    encodings_norm = (encodings - min_encoding) / (max_encoding - min_encoding)\n",
    "    timestretches = []\n",
    "    for encoding_i in encodings_norm:\n",
    "        stretched = resize(encoding_i, (int(encoding_i.shape[0] * factor), encoding_i.shape[1]), mode='reflect')\n",
    "        stretched = (stretched * (max_encoding - min_encoding)) + min_encoding\n",
    "        timestretches.append(stretched)\n",
    "    return np.array(timestretches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model.ckpt-200000\n"
     ]
    }
   ],
   "source": [
    "# from https://www.freesound.org/people/MustardPlug/sounds/395058/\n",
    "fname = '395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav'\n",
    "sample_length = 40000\n",
    "audio, encoding = load_encoding(fname, sample_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's stretch the encodings with a few different factors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '/Users/gulli/magenta/demos/nsynth/gen_slower_395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-d6008e74b59f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gen_slower_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages/magenta/models/nsynth/utils.pyc\u001b[0m in \u001b[0;36mload_audio\u001b[0;34m(path, sample_length, sr)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0maudio\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0mto\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m   \"\"\"\n\u001b[0;32m---> 64\u001b[0;31m   \u001b[0maudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m   \u001b[0maudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msample_length\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages/librosa/core/audio.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, sr, mono, offset, duration, dtype, res_type)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0maudioread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudio_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrealpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0msr_native\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamplerate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0mn_channels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages/audioread/__init__.pyc\u001b[0m in \u001b[0;36maudio_open\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrawread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mrawread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRawAudioFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gulli/miniconda2/envs/magenta/lib/python2.7/site-packages/audioread/rawread.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \"\"\"\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/Users/gulli/magenta/demos/nsynth/gen_slower_395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav'"
     ]
    }
   ],
   "source": [
    "audio = utils.load_audio('gen_slower_' + fname, sample_length=None, sr=sr)\n",
    "Audio(audio, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoding_slower = timestretch(encoding, 1.5)\n",
    "encoding_faster = timestretch(encoding, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically we've made a slower and faster version of the amen break's encodings.  The original encoding is shown in black:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 7), sharex=True, sharey=True)\n",
    "axs[0].plot(encoding[0]); \n",
    "axs[0].set_title('Encoding (Normal Speed)')\n",
    "axs[1].plot(encoding_faster[0]);\n",
    "axs[1].set_title('Encoding (Faster))')\n",
    "axs[2].plot(encoding_slower[0]);\n",
    "axs[2].set_title('Encoding (Slower)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's decode them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fastgen.synthesize(encoding_faster, save_paths=['gen_faster_' + fname])\n",
    "fastgen.synthesize(encoding_slower, save_paths=['gen_slower_' + fname])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems to work pretty well and retains the pitch and timbre of the original sound.  We could even quickly layer the sounds just by adding them.  You might want to do this in a program like Logic or Ableton Live instead and explore more possiblities of these sounds!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Interpolating Sounds\n",
    "\n",
    "Now let's try something more experimental.  NSynth released plenty of great examples of what happens when you mix the embeddings of different sounds: https://magenta.tensorflow.org/nsynth-instrument - we're going to do the same but now with our own sounds!\n",
    "\n",
    "First let's load some encodings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_length = 80000\n",
    "\n",
    "# from https://www.freesound.org/people/MustardPlug/sounds/395058/\n",
    "aud1, enc1 = load_encoding('395058__mustardplug__breakbeat-hiphop-a4-4bar-96bpm.wav', sample_length)\n",
    "\n",
    "# from https://www.freesound.org/people/xserra/sounds/176098/\n",
    "aud2, enc2 = load_encoding('176098__xserra__cello-cant-dels-ocells.wav', sample_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll mix the two audio signals together.  But this is unlike adding the two signals together in a Ableton or simply hearing both sounds at the same time.  Instead, we're averaging the representation of their timbres, tonality, change over time, and resulting audio signal.  This is way more powerful than a simple averaging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_mix = (enc1 + enc2) / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 7))\n",
    "axs[0].plot(enc1[0]); \n",
    "axs[0].set_title('Encoding 1')\n",
    "axs[1].plot(enc2[0]);\n",
    "axs[1].set_title('Encoding 2')\n",
    "axs[2].plot(enc_mix[0]);\n",
    "axs[2].set_title('Average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fastgen.synthesize(enc_mix, save_paths='mix.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another example of what's possible with interpolation of embeddings, we'll try crossfading between the two embeddings.  To do this, we'll write a utility function which will use a hanning window to apply a fade in or out to the embeddings matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fade(encoding, mode='in'):\n",
    "    length = encoding.shape[1]\n",
    "    fadein = (0.5 * (1.0 - np.cos(3.1415 * np.arange(length) / \n",
    "                                  float(length)))).reshape(1, -1, 1)\n",
    "    if mode == 'in':\n",
    "        return fadein * encoding\n",
    "    else:\n",
    "        return (1.0 - fadein) * encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 7))\n",
    "axs[0].plot(enc1[0]); \n",
    "axs[0].set_title('Original Encoding')\n",
    "axs[1].plot(fade(enc1, 'in')[0]);\n",
    "axs[1].set_title('Fade In')\n",
    "axs[2].plot(fade(enc1, 'out')[0]);\n",
    "axs[2].set_title('Fade Out')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can cross fade two different encodings by adding their repsective fade ins and out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def crossfade(encoding1, encoding2):\n",
    "    return fade(encoding1, 'out') + fade(encoding2, 'in')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 7))\n",
    "axs[0].plot(enc1[0]); \n",
    "axs[0].set_title('Encoding 1')\n",
    "axs[1].plot(enc2[0]);\n",
    "axs[1].set_title('Encoding 2')\n",
    "axs[2].plot(crossfade(enc1, enc2)[0]);\n",
    "axs[2].set_title('Crossfade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's synthesize the resulting encodings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fastgen.synthesize(crossfade(enc1, enc2), save_paths=['crossfade.wav'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot to explore with NSynth.  So far I've just shown you a taste of what's possible when you are able to generate your own sounds.  I expect the generation process will soon get much faster, especially with help from the community, and for more unexpected and interesting applications to emerge.  Please keep in touch with whatever you end up creating, either personally via [twitter](https://twitter.com/pkmital), in our [Creative Applications of Deep Learning](https://www.kadenze.com/programs/creative-applications-of-deep-learning-with-tensorflow) community on Kadenze, or the [Magenta Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/magenta-discuss)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
